The present invention relates to a scanning converter for converting the scanning method for a television signal from an interlaced scanning method, where adjacent lines are formed in different scanning passes, into a progressive scanning method where adjacent lines are sequentially formed in the same scan pass, and to a method therefor. More particularly, the present invention relates to an interlaced-to-progressive scanning converter and method which prevents picture quality deterioration by employing a double-smoother using median-filtering, which reduces the distortion effects associated with eliminating noise.
In general, in an image signal processor such as is used in televisions, facsimiles or medical appliances, an image signal based on an interlaced scanning method is converted into a progressive scanning signal to improve the picture quality which is somewhat distorted by the use of interlaced scanning. In the case of a television signal, the interlaced scanning method provides for effective utilization of transmission bands and takes advantage of the physical properties of a television receiver. However, the interlaced scanning method produces deterioration phenomena such as inter-line flickering or line crawling. Moreover, with large high-quality television screens, scanning lines can be seen on a screen along with such deterioration phenomena.
In order to alleviate the deterioration of the picture quality due to the interlaced scanning method as described above, the television signal scanning method is converted into a progressive scanning method.
Previously, interlaced-to-progressive scanning converters processed a signal on horizontal and vertical space planes. However, due to the recent quest for a high quality television picture and the reduction of memory cost in connection therewith, motion-adaptive signal processing has been employed for high picture quality televisions.
FIG. 1 shows an example of an interlaced-to-progressive converter which performs the conversion by linear interpolation using a vertical median, or in other words, the median value of pixels aligned vertically but in different lines of the picture. The converter delays the interlaced scanning signal by a 1H delay element 11 and applies the delayed signal to a first input terminal of a selector 14. Here, an adder 12 adds the input interlaced scanning signal to the 1H delayed signal and applies the resultant signal to a second input terminal of the selector 14 through a divide-by-two circuit 13 thus, producing a pixel which is the median value between the interlaced scanning signal and the 1H delayed signal. Then, selector 14 alternately selects the 1H delayed signal and the divide-by-two median signal at a rate twice the interlaced scanning rate, to thereby output a progressive scanning signal. However, the interlaced-to-progressive scanning converter shown in FIG. 1 may cause the displayed image to be blurry.
Also, an interlaced-to-progressive scanning converter which performs a conversion by three-dimensional interpolation using motion-adaptive signal processing, improves the resolution of motionless areas of an image but cannot prevent the phenomenon of picture contour deterioration in which contours contained in a picture appear as step-shaped edges. To avoid this problem, a separate motion detector must be used, however, the associated hardware becomes complex due to the necessity for using a field memory which stores an entire field, or in other words, one pass of an interlace scan, resulting in increased cost.
Furthermore, in the case of a transmitted image signal or a signal reproduced from a recorded image signal, picture quality is also deteriorated due to the mixing of impulse noise or Gaussian noise with the signal within a given channel. Impulse noise within a picture signal may be generated, for example, by a low signal-to-noise ratio of an FM satellite broadcast signal, or by electromagnetic interference in the television receiver. In this case, when performing motion-adaptive signal processing using motion detection, the picture which includes a noise signal may cause a malfunction during detection. This is because an analog correlation, which can be indicated by the difference in levels between a current frame and a previous frame, is generated and the extent of motion calculated based on the correlation, which can be corrupted by noise. Thus, even though interlaced-to-progressive scanning conversion is performed, a deteriorated picture may result due to the presence of noise.
To solve the aforementioned problems, an interlaced-to-progressive scanning converting method in which a median filter having no motion detecting capability but being capable of effectively preventing noise, has been proposed by Licia Capodiferro, Interlaced-to-progressive Conversion by Median-filtering, Proceedings of the 3rd International Workshop on HDTV (Torino, Italy, September 1989). Here, the median filter determines a median value between adjacent data, and thus, provides a simple hardware implementation of such a converter. However, such a converting method using a median filter results in a stepped-shaped edge phenomenon for contours in the picture, which is more apparent than with the method using motion-adaptive signal processing. Furthermore, if noise is mixed on the channel, the pixels corresponding to the noise components are used in the interpolation, resulting in a lower signal-to-noise ratio than in the case of the interlaced-to-progressive scanning converting method using linear interpolation.
To compensate for such shortcomings, a finite impulse response (FIR) filter can be used together with the median filter for pre-processing the interlaced-to-progressive conversion. The FIR filter effectively eliminates the Gaussian noise, but does not effectively eliminate the impulse noise. On the other hand, the median filter effectively eliminates the impulse noise but not the Gaussian noise. Accordingly, as shown in FIG. 2, a double-smoothing method performed by a combination of a median filter and FIR filter has been proposed by L. R. Rabiner, M. R. Sambur and C. E. Schmidt, Applications of a Non-linear Smoothing Algorithm for Speech Processing, IEEE Trans. on ASSP, Vol. 23, pp. 552-557 (December 1975).
As shown in FIG. 2, the impulse noise and Gaussian noise components of the interlaced scanning signal x.sub.k are filtered by the median filter 21 and the FIR filter 22, respectively. The input signal x.sub.k, is delayed in delay element 23 for a predetermined time, and subtracted from the filtered signal in a subtractor 24 to thereby obtain an error signal that corresponds to the noise components. The error signal is again filtered by median filter 25 and FIR filter 26. Thus, the resulting output signal from FIR filter 26 contains the signal extracted from the input noise components and, hence, provides a double-filtered source signal. This source signal and a source signal output from the FIR filter 22, which passes through delay element 27, are summed in an adder 28 to thereby obtain a final double-smoothed source signal s.sub.k. However, this interlaced-to-progressive scanning converter employing such a double-smoothing method in processing a picture signal, causes the picture signal to tend to be excessively repressed, and stepped-shaped edges still remain due to bias errors generated in the median filter that are produced from edge portions in the picture.
Further, an adaptive median filter system is disclosed in U.S. Pat. No. 4,682,230 wherein the relative density of an impulse noise component included in an input signal is detected. Here, a control signal corresponding to the detected noise density is generated, and a signal sampled is adaptively filtered according to the control signal.